Showing 28 of total 28 results (show query)
stan-dev
rstan:R Interface to Stan
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Maintained by Ben Goodrich. Last updated 12 hours ago.
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstancpp
1.1k stars 18.86 score 14k scripts 281 dependents
suyusung
arm:Data Analysis Using Regression and Multilevel/Hierarchical Models
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
Maintained by Yu-Sung Su. Last updated 5 months ago.
25 stars 12.38 score 3.3k scripts 89 dependentssuyusung
R2jags:Using R to Run 'JAGS'
Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
Maintained by Yu-Sung Su. Last updated 5 months ago.
8 stars 11.39 score 3.4k scripts 47 dependents
martynplummer
coda:Output Analysis and Diagnostics for MCMC
Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain.
Maintained by Martyn Plummer. Last updated 1 years ago.
6 stars 11.38 score 8.3k scripts 1.1k dependents
kenkellner
jagsUI:A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
Maintained by Ken Kellner. Last updated 2 months ago.
35 stars 9.99 score 1.4k scripts 7 dependents
nlmixr2
nlmixr2:Nonlinear Mixed Effects Models in Population PK/PD
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Maintained by Matthew Fidler. Last updated 1 months ago.
52 stars 8.38 score 120 scripts 3 dependents
drizopoulos
JMbayes2:Extended Joint Models for Longitudinal and Time-to-Event Data
Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).
Maintained by Dimitris Rizopoulos. Last updated 24 days ago.
competing-riskslongitudinal-analysismixed-modelsmulti-statepersonalized-medicineprecision-medicineprediction-modelsurvival-modelsopenblascppopenmp
84 stars 8.27 score 264 scripts 2 dependents
biodiverse
ubms:Bayesian Models for Data from Unmarked Animals using 'Stan'
Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 1 months ago.
distance-samplinghierarchical-modelsn-mixture-modeloccupancystanopenblascpp
36 stars 7.90 score 73 scripts
rezamoammadi
BDgraph:Bayesian Structure Learning in Graphical Models using Birth-Death MCMC
Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.
Maintained by Reza Mohammadi. Last updated 7 months ago.
8 stars 7.46 score 223 scripts 7 dependents
nerler
JointAI:Joint Analysis and Imputation of Incomplete Data
Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.
Maintained by Nicole S. Erler. Last updated 12 months ago.
bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp
28 stars 7.30 score 59 scripts 1 dependents
joliencremers
bpnreg:Bayesian Projected Normal Regression Models for Circular Data
Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>.
Maintained by Jolien Cremers. Last updated 1 years ago.
14 stars 6.15 score 101 scripts
matthieu-bruneaux
isotracer:Isotopic Tracer Analysis Using MCMC
Implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) <doi:10.1086/708546>.
Maintained by Matthieu Bruneaux. Last updated 5 months ago.
5.92 score 60 scripts
cszang
treeclim:Numerical Calibration of Proxy-Climate Relationships
Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.
Maintained by Christian Zang. Last updated 4 months ago.
climate-relationshipsdendroclimatologydendroecologyopenblascpp
18 stars 5.66 score 36 scripts
staffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 4 months ago.
5.42 score 4.4k scripts
christianroever
bayesmeta:Bayesian Random-Effects Meta-Analysis and Meta-Regression
A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022) <doi:10.1016/j.cmpb.2022.107303>.
Maintained by Christian Roever. Last updated 1 years ago.
3 stars 5.40 score 73 scripts 1 dependents
bgoussen
BeeGUTS:General Unified Threshold Model of Survival for Bees using Bayesian Inference
Tools to calibrate, validate, and make predictions with the General Unified Threshold model of Survival adapted for Bee species. The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) <doi:10.1002/etc.5423> and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) <doi:10.1002/etc.5871>, and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) <doi:10.1021/es103092a>. The authors are grateful to Bayer A.G. for its financial support.
Maintained by Benoit Goussen. Last updated 5 months ago.
3 stars 4.95 score 6 scriptsnlmixr2
nlmixr2plot:Nonlinear Mixed Effects Models in Population PK/PD, Plot Functions
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for 'ggplot2' plotting methods for 'nlmixr2' objects.
Maintained by Matthew Fidler. Last updated 1 months ago.
2 stars 4.93 score 6 scripts 4 dependents
friendly
genridge:Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Maintained by Michael Friendly. Last updated 4 months ago.
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
4 stars 4.84 score 69 scripts
datacloning
dcmle:Hierarchical Models Made Easy with Data Cloning
S4 classes around infrastructure provided by the 'coda' and 'dclone' packages to make package development easy as a breeze with data cloning for hierarchical models.
Maintained by Peter Solymos. Last updated 6 months ago.
4.60 score 66 scripts 2 dependents
brechtdv
prevalence:Tools for Prevalence Assessment Studies
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from <https://mcmc-jags.sourceforge.io/>.
Maintained by Brecht Devleesschauwer. Last updated 3 years ago.
2 stars 4.48 score 38 scripts
joshcullen
bayesmove:Non-Parametric Bayesian Analyses of Animal Movement
Methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.
Maintained by Joshua Cullen. Last updated 1 years ago.
9 stars 4.18 score 34 scripts
laura-dangelo
SANple:Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo
Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.
Maintained by Francesco Denti. Last updated 6 months ago.
3.48 score 3 scripts
cran
catalytic:Tools for Applying Catalytic Priors in Statistical Modeling
To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).
Maintained by Dongming Huang. Last updated 3 months ago.
3.18 score
giabaio
bmhe:This Package Creates a Set of Functions Useful for Bayesian modelling
A set of utility functions that can be used to post-process BUGS or JAGS objects as well as other to facilitate various Bayesian modelling activities (including in HTA).
Maintained by Gianluca Baio. Last updated 24 days ago.
bayesian-statisticsbugscost-effectiveness-analysisjagstidyverse
2 stars 3.00 score 7 scripts
vallejosgroup
bayefdr:Bayesian Estimation and Optimisation of Expected False Discovery Rate
Implements the Bayesian FDR control described by Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>. Allows optimisation and visualisation of expected error rates based on tail posterior probability tests. Based on code written by Catalina Vallejos for BASiCS, see Beyond comparisons of means: understanding changes in gene expression at the single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.
Maintained by Alan OCallaghan. Last updated 3 years ago.
2.70 score 1 scripts
rwehrens
BioMark:Find Biomarkers in Two-Class Discrimination Problems
Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Maintained by Ron Wehrens. Last updated 10 years ago.
2.32 score 21 scriptscran
BNPdensity:Ferguson-Klass Type Algorithm for Posterior Normalized Random Measures
Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.
Maintained by Guillaume Kon Kam King. Last updated 2 years ago.
1.00 score